P, N y Caudal

Observaciones:

En la imagen, donde los channels estan en azul y la subcuenca en negro, se nota que no están incluidos todos los channels.

Canales y Subcuencas del archivo hru2.shp

Salidas

Notar que el nivel permitido por dinama es de hasta 0.25 mg/L en P y 10 mg/L en N. A continuación los resultados por canal y subcuenca.

dt1<-
env_out_sub %>% group_by(channel, Subbasin) %>% summarise(P_mean=mean(P_Concentration),                                  N_mean=mean(N_Concentration),                                  flo_out_mean=mean(flo_out)) %>%
          arrange(desc(P_mean)); dt1
## # A tibble: 95 x 5
## # Groups:   channel [95]
##    channel Subbasin      P_mean    N_mean flo_out_mean
##      <dbl>    <dbl>       <dbl>     <dbl>        <dbl>
##  1      95       NA 227070.     2201.             2.66
##  2      86        3     59.1       1.45           3.32
##  3      84       NA     44.6       9.16           3.47
##  4      91       NA     25.2       0.296          2.53
##  5      93       NA      1.65      0.274          2.44
##  6      92        2      0.241     0.409          2.52
##  7      82       NA      0.227     0.0763         5.47
##  8      51        4      0.182     7.11          71.1 
##  9      80        8      0.169     0.260          3.97
## 10      11       12      0.0658    0.690       1172.  
## # ... with 85 more rows
env_out_sub %>% group_by(channel, Subbasin) %>% summarise(P_mean=mean(P_Concentration),                                  N_mean=mean(N_Concentration),                                  flo_out_mean=mean(flo_out)) %>%
          arrange(desc(N_mean))
## # A tibble: 95 x 5
## # Groups:   channel [95]
##    channel Subbasin      P_mean  N_mean flo_out_mean
##      <dbl>    <dbl>       <dbl>   <dbl>        <dbl>
##  1      95       NA 227070.     2201.           2.66
##  2      40       10      0.0191   10.6        140.  
##  3      84       NA     44.6       9.16         3.47
##  4      48       10      0.0151    9.03        96.0 
##  5      51        4      0.182     7.11        71.1 
##  6      44       NA      0.0272    6.60       176.  
##  7      20       NA      0.0198    4.61       438.  
##  8      45       NA      0.0190    4.06       112.  
##  9      57       NA      0.0157    3.49        49.1 
## 10      19       11      0.0141    3.45       449.  
## # ... with 85 more rows
env_out_sub %>% group_by(channel, Subbasin) %>% summarise(P_mean=mean(P_Concentration),                                  N_mean=mean(N_Concentration),                                  flo_out_mean=mean(flo_out)) %>%
          arrange(desc(flo_out_mean))
## # A tibble: 95 x 5
## # Groups:   channel [95]
##    channel Subbasin   P_mean N_mean flo_out_mean
##      <dbl>    <dbl>    <dbl>  <dbl>        <dbl>
##  1       1       13 0.000236 0.0918        1372.
##  2       2       13 0.000457 0.154         1355.
##  3       3       13 0.000544 0.168         1353.
##  4       4       NA 0.000555 0.226         1293.
##  5       5       13 0.000204 0.0698        1289.
##  6       6       NA 0.000199 0.0677        1282.
##  7       7       NA 0.00180  0.0987        1274.
##  8       8       12 0.00127  0.0846        1227.
##  9       9       12 0.000869 0.0767        1201.
## 10      10       NA 0.00133  0.0790        1182.
## # ... with 85 more rows
env_out_sub %>% group_by(channel, Subbasin) %>% summarise(P_mean=mean(P_Concentration),                                  N_mean=mean(N_Concentration),                                  flo_out_mean=mean(flo_out)) %>%
          arrange(flo_out_mean)
## # A tibble: 95 x 5
## # Groups:   channel [95]
##    channel Subbasin        P_mean   N_mean flo_out_mean
##      <dbl>    <dbl>         <dbl>    <dbl>        <dbl>
##  1      94        8      0.000704    0.146         2.10
##  2      90       NA      0.000381    0.166         2.26
##  3      93       NA      1.65        0.274         2.44
##  4      92        2      0.241       0.409         2.52
##  5      91       NA     25.2         0.296         2.53
##  6      95       NA 227070.       2201.            2.66
##  7      86        3     59.1         1.45          3.32
##  8      84       NA     44.6         9.16          3.47
##  9      89        8      0.000509    0.121         3.87
## 10      80        8      0.169       0.260         3.97
## # ... with 85 more rows
plotly::ggplotly(
ggplot(data=dt1 %>% filter(channel!=95), aes(x=channel, y=N_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)
plotly::ggplotly(
ggplot(data=dt1 %>% filter(channel!=95), aes(x=channel, y=P_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)
plotly::ggplotly(
ggplot(data=dt1 %>% filter(P_mean<10), aes(x=channel, y=P_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)
plotly::ggplotly(
ggplot(data=dt1, aes(x=channel, y=flo_out_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)

Los resultados solo por subcuenca son los siguientes:

#sub promedio N, P, Flo por subbasin ----
dt2<- env_out_sub %>% group_by(Subbasin) %>% summarise(P_mean=mean(P_Concentration),                                              N_mean=mean(N_Concentration),
                      flo_out_mean=mean(flo_out)) %>%
  arrange(desc(P_mean)) %>% 
  filter(!is.na(Subbasin)) %>%
  mutate(indice_N=N_mean/max(N_mean),
         indice_P=P_mean/max(P_mean),
         indice_flo=flo_out_mean/max(flo_out_mean)); dt2
## # A tibble: 9 x 7
##   Subbasin    P_mean N_mean flo_out_mean indice_N   indice_P indice_flo
##      <dbl>     <dbl>  <dbl>        <dbl>    <dbl>      <dbl>      <dbl>
## 1        3 59.1      1.45           3.32   0.367  1             0.00247
## 2        2  0.241    0.409          2.52   0.103  0.00407       0.00188
## 3        8  0.0293   0.147          5.41   0.0370 0.000496      0.00403
## 4        4  0.0282   1.71          37.1    0.432  0.000477      0.0276 
## 5       12  0.0226   0.284       1200.     0.0717 0.000383      0.894  
## 6       10  0.00700  3.96         137.     1      0.000118      0.102  
## 7       11  0.00343  0.752        434.     0.190  0.0000581     0.324  
## 8       13  0.000360 0.121       1342.     0.0305 0.00000610    1      
## 9        9  0.000185 0.0841         6.07   0.0212 0.00000314    0.00452
dt3<- env_out_sub %>% group_by(yr) %>% summarise(P_mean=mean(P_Concentration),                                              N_mean=mean(N_Concentration),
                      flo_out_mean=mean(flo_out)) %>%
  mutate(indice_N=N_mean/max(N_mean),
         indice_P=P_mean/max(P_mean),
         indice_flo=flo_out_mean/max(flo_out_mean)) %>%
  mutate(yr=as.numeric(yr)) %>% arrange(yr); dt3
## # A tibble: 20 x 7
##       yr       P_mean   N_mean flo_out_mean indice_N      indice_P indice_flo
##    <dbl>        <dbl>    <dbl>        <dbl>    <dbl>         <dbl>      <dbl>
##  1  2000     0.000481   0.104        392.   0.000223 0.0000000101      0.724 
##  2  2001     0.000311   0.0780       454.   0.000168 0.00000000651     0.839 
##  3  2002     0.000259   0.130        274.   0.000279 0.00000000541     0.505 
##  4  2003     0.000316   0.0700       319.   0.000151 0.00000000662     0.590 
##  5  2004     0.000359   0.160        148.   0.000345 0.00000000751     0.274 
##  6  2005     0.000307   0.0961       231.   0.000207 0.00000000642     0.426 
##  7  2006     0.000230   0.103        224.   0.000222 0.00000000482     0.413 
##  8  2007     0.000357   0.150        464.   0.000323 0.00000000747     0.856 
##  9  2008    21.7        1.21           9.86 0.00260  0.000454          0.0182
## 10  2009 47805.       465.           329.   1        1                 0.608 
## 11  2010     0.000363   0.122        323.   0.000262 0.00000000760     0.597 
## 12  2011     0.000341   0.0761        76.3  0.000164 0.00000000714     0.141 
## 13  2012     0.0446     0.0840       326.   0.000181 0.000000932       0.603 
## 14  2013     0.0745    12.7          105.   0.0273   0.00000156        0.194 
## 15  2014     0.000266   0.105        541.   0.000227 0.00000000557     1     
## 16  2015     0.000349   0.0487       158.   0.000105 0.00000000729     0.292 
## 17  2016     0.000404   0.118        286.   0.000254 0.00000000846     0.528 
## 18  2017     0.000418   0.0559       259.   0.000120 0.00000000875     0.479 
## 19  2018     5.36       0.121        230.   0.000261 0.000112          0.425 
## 20  2019     0.000492   0.0528       330.   0.000114 0.0000000103      0.609
plotly::ggplotly(
ggplot(data=dt2, aes(x=Subbasin, y=N_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)
plotly::ggplotly(
ggplot(data=dt2, aes(x=Subbasin, y=P_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)
plotly::ggplotly(
ggplot(data=dt2 %>% filter(P_mean<20), aes(x=Subbasin, y=P_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)
plotly::ggplotly(
ggplot(data=dt2, aes(x=Subbasin, y=flo_out_mean, fill=Subbasin)) +
  geom_bar(stat="identity")+
  coord_flip()
)
plotly::ggplotly(
ggplot(data=dt1 %>% filter(!is.na(Subbasin)), aes(x=Subbasin, y=N_mean)) +
  geom_boxplot()
)
plotly::ggplotly(
ggplot(data=dt1 %>% filter(!is.na(Subbasin)) %>% filter(P_mean<20), aes(x=Subbasin, y=P_mean)) +
  geom_boxplot()
)
plotly::ggplotly(
ggplot(data=env_out_sub  %>% filter(P_Concentration<10), aes(x=yr, y=P_Concentration)) +
geom_boxplot()

)
plotly::ggplotly(
ggplot(data=env_out_sub  %>% filter(N_Concentration<100), aes(x=yr, y=N_Concentration)) +
geom_boxplot()

)
plotly::ggplotly(
ggplot(data=env_out_sub , aes(x=yr, y=flo_out)) +
geom_boxplot()

)
plotly::ggplotly(
ggplot(data=dt1 %>% filter(!is.na(Subbasin)) , aes(x=Subbasin, y=flo_out_mean)) +
geom_boxplot()

)

Riego

irr_yr<-
  irr_yr %>% plyr::join(hru_info, by="hru") %>% plyr::join(sub_hru, by="hru")
irr1<-
irr_yr %>% group_by(Subbasin) %>%
  summarise(irr=sum(irr_sum)) %>% arrange(desc(irr));irr1
## # A tibble: 14 x 2
##    Subbasin     irr
##       <dbl>   <dbl>
##  1       NA 457515 
##  2        8 183015 
##  3        6 181845 
##  4       10 154260 
##  5       13 149355 
##  6        2 135810 
##  7        4 135652.
##  8        9 123390 
##  9        5 111780 
## 10        1  75555 
## 11       12  61830 
## 12       11  59040 
## 13        7  56992.
## 14        3  35258.
plotly::ggplotly(
ggplot(data=irr_yr %>% filter(!is.na(Subbasin)) , aes(x=Subbasin, y=irr_sum)) +
  geom_boxplot()
)
irr2<-
irr_yr %>% group_by(lu_mgt) %>%
  summarise(irr=sum(irr_sum)); irr2
## # A tibble: 2 x 2
##   lu_mgt        irr
##   <chr>       <dbl>
## 1 agrc3_lum 935190 
## 2 agrc4_lum 986108.
plotly::ggplotly(
ggplot(data=irr_yr  , aes(x=lu_mgt, y=irr_sum)) +
 geom_boxplot()
)
irr3<-
irr_yr %>% group_by(yr) %>%
  summarise(irr=sum(irr_sum)); irr3
## # A tibble: 20 x 2
##    yr        irr
##    <chr>   <dbl>
##  1 2000  197662.
##  2 2001   43402.
##  3 2002   43470 
##  4 2003     270 
##  5 2004   51210 
##  6 2005  108945 
##  7 2006   67230 
##  8 2007   77130 
##  9 2008  214695 
## 10 2009  142718.
## 11 2010   72810 
## 12 2011  221918.
## 13 2012  166275 
## 14 2013   54292.
## 15 2014    2272.
## 16 2015   88650 
## 17 2016   63202.
## 18 2017   58792.
## 19 2018  191992.
## 20 2019   54360
plotly::ggplotly(
ggplot(data=irr_yr  , aes(x=yr, y=irr_sum)) +
   geom_boxplot()
)
irr4<-
irr_yr %>% group_by(Subbasin, lu_mgt) %>%
  summarise(irr=sum(irr_sum)); irr4
## # A tibble: 28 x 3
## # Groups:   Subbasin [14]
##    Subbasin lu_mgt       irr
##       <dbl> <chr>      <dbl>
##  1        1 agrc3_lum 36472.
##  2        1 agrc4_lum 39082.
##  3        2 agrc3_lum 66960 
##  4        2 agrc4_lum 68850 
##  5        3 agrc3_lum 16065 
##  6        3 agrc4_lum 19192.
##  7        4 agrc3_lum 66262.
##  8        4 agrc4_lum 69390 
##  9        5 agrc3_lum 53505 
## 10        5 agrc4_lum 58275 
## # ... with 18 more rows
plotly::ggplotly(
ggplot(data=irr_yr, aes(x=Subbasin, y=irr_sum, fill=lu_mgt)) +
  geom_bar(stat="identity")
)